{
 "cells": [
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   "source": [
    "## Expert Knowledge Worker\n",
    "\n",
    "### A question answering agent that is an expert knowledge worker\n",
    "### To be used by Anyone on their LinkedIn data\n",
    "The easiest and fastest way to obtain a copy of your LinkedIn data is to initiate a data download from your Settings & Privacy page:\n",
    "\n",
    "1. Click the  Me icon at the top of your LinkedIn homepage.\n",
    "2. Select Settings & Privacy from the dropdown.\n",
    "3. Click the Data Privacy on the left rail.\n",
    "4 .Under the How LinkedIn uses your data section, click Get a copy of your data.\n",
    "5. Select the data that you’re looking for and Request archive.\n",
    "\n",
    "This project will use RAG (Retrieval Augmented Generation) to ensure our question/answering assistant has high accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba2779af-84ef-4227-9e9e-6eaf0df87e77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import os\n",
    "import glob\n",
    "from dotenv import load_dotenv\n",
    "import gradio as gr\n",
    "\n",
    "from langchain.document_loaders import DirectoryLoader, TextLoader\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.schema import Document\n",
    "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n",
    "from langchain_chroma import Chroma\n",
    "import plotly.graph_objects as go\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "from langchain.chains import ConversationalRetrievalChain\n",
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "import numpy as np\n",
    "\n",
    "MODEL = \"gpt-4o-mini\"\n",
    "db_name = \"linkedin_db\"\n",
    "\n",
    "load_dotenv(override=True)\n",
    "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "730711a9-6ffe-4eee-8f48-d6cfb7314905",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in documents using LangChain's loaders\n",
    "# Put the chunks of data into a Vector Store (Chroma) that associates a Vector Embedding with each chunk\n",
    "\n",
    "folders = glob.glob(\"linkedin-base/*\")\n",
    "\n",
    "def add_metadata(doc, doc_type):\n",
    "    doc.metadata[\"doc_type\"] = doc_type\n",
    "    return doc\n",
    "\n",
    "text_loader_kwargs = {'encoding': 'utf-8'}\n",
    "\n",
    "documents = []\n",
    "for folder in folders:\n",
    "    doc_type = os.path.basename(folder)\n",
    "    loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n",
    "    folder_docs = loader.load()\n",
    "    documents.extend([add_metadata(doc, doc_type) for doc in folder_docs])\n",
    "\n",
    "text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
    "chunks = text_splitter.split_documents(documents)\n",
    "\n",
    "embeddings = OpenAIEmbeddings()\n",
    "\n",
    "if os.path.exists(db_name):\n",
    "    Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n",
    "\n",
    "vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n",
    "\n",
    "collection = vectorstore._collection\n",
    "count = collection.count()\n",
    "\n",
    "sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n",
    "dimensions = len(sample_embedding)\n",
    "\n",
    "\n",
    "print(f\"Total number of chunks: {len(chunks)}\")\n",
    "print(f\"Document types found: {set(doc.metadata['doc_type'] for doc in documents)}\")\n",
    "print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")\n",
    "print(f\"There are {count:,} vectors with {dimensions:,} dimensions in the vector store\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b98adf5e-d464-4bd2-9bdf-bc5b6770263b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2D scatter plot\n",
    "\n",
    "result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n",
    "vectors = np.array(result['embeddings'])\n",
    "documents = result['documents']\n",
    "metadatas = result['metadatas']\n",
    "doc_types = [metadata['doc_type'] for metadata in metadatas]\n",
    "colors = [['blue', 'green', 'red'][['connections', 'recommendations', 'profiles'].index(t)] for t in doc_types]\n",
    "\n",
    "n = vectors.shape[0]\n",
    "if n < 3:\n",
    "    raise ValueError(f\"t-SNE needs at least 3 samples, got {n}\")\n",
    "\n",
    "perp = max(5.0, min(30.0, (n - 1) / 3.0))  # always < n, within [5, 30]\n",
    "\n",
    "tsne = TSNE(n_components=2, random_state=42, perplexity=perp)\n",
    "reduced_vectors = tsne.fit_transform(vectors)\n",
    "\n",
    "fig = go.Figure(data=[go.Scatter(\n",
    "    x=reduced_vectors[:, 0],\n",
    "    y=reduced_vectors[:, 1],\n",
    "    mode='markers',\n",
    "    marker=dict(size=5, color=colors, opacity=0.8),\n",
    "    text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n",
    "    hoverinfo='text'\n",
    ")])\n",
    "\n",
    "fig.update_layout(\n",
    "    title='2D Chroma Vector Store Visualization',\n",
    "    scene=dict(xaxis_title='x',yaxis_title='y'),\n",
    "    width=800,\n",
    "    height=600,\n",
    "    margin=dict(r=20, b=10, l=10, t=40)\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1418e88-acd5-460a-bf2b-4e6efc88e3dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3D scatter plot!\n",
    "\n",
    "n = vectors.shape[0]\n",
    "if n < 3:\n",
    "    raise ValueError(f\"t-SNE needs at least 3 samples, got {n}\")\n",
    "\n",
    "perp = max(5.0, min(30.0, (n - 1) / 3.0))\n",
    "\n",
    "tsne = TSNE(n_components=3, random_state=42, perplexity=perp)\n",
    "reduced_vectors = tsne.fit_transform(vectors)\n",
    "\n",
    "fig = go.Figure(data=[go.Scatter3d(\n",
    "    x=reduced_vectors[:, 0],\n",
    "    y=reduced_vectors[:, 1],\n",
    "    z=reduced_vectors[:, 2],\n",
    "    mode='markers',\n",
    "    marker=dict(size=5, color=colors, opacity=0.8),\n",
    "    text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n",
    "    hoverinfo='text'\n",
    ")])\n",
    "\n",
    "fig.update_layout(\n",
    "    title='3D Chroma Vector Store Visualization',\n",
    "    scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n",
    "    width=900,\n",
    "    height=700,\n",
    "    margin=dict(r=20, b=10, l=10, t=40)\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2136153b-d2f6-4c58-a0e3-78c3a932cf55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The main Langchain Abstraction are:  Memory, LLM, and Retriever\n",
    "llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n",
    "\n",
    "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
    "retriever = vectorstore.as_retriever(search_kwargs={\"k\": 25})\n",
    "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)\n",
    "\n",
    "def chat(question, history):\n",
    "    result = conversation_chain.invoke({\"question\": question})\n",
    "    return result[\"answer\"]\n",
    "\n",
    "with gr.Blocks(theme=\"gradio/monochrome\") as ui:\n",
    "    gr.Markdown(\n",
    "        \"\"\"\n",
    "        <h2 style=\"color: #f5f5f5;\">Linkedin Knowledge Worker</h2>\n",
    "        <p style=\"color: #f5f5f5;\">Chat with your auto-generated Linkedin knowledge base </p>\n",
    "        \"\"\",\n",
    "        elem_id=\"title\"\n",
    "    )\n",
    "    gr.ChatInterface(chat, type=\"messages\")\n",
    "\n",
    "ui.launch(inbrowser=True)"
   ]
  }
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